Work order running late?
AI knows why — and how to fix it.
AI Production Agent reviews your work orders, MRP material status, machine OEE, and quality incidents every day — and tells you where the bottlenecks are and how to resolve them.
Production without surprises
MRP shortage alerts with alternative suggestions
AI detects material shortages for every work order in real time and immediately suggests alternative materials from stock with matching specifications.
Real-time OEE monitoring
Availability, performance, and quality for every machine — AI detects deviation from target OEE and triggers an alert before the loss becomes measurable.
Work order delay prediction
The model recognises delay patterns 48 hours in advance — based on material status, machine capacity, and order priority — and suggests rescheduling.
Bottleneck identification
AI analyses order flow through every work centre and highlights which machine or operation is blocking the entire line — with an impact estimate in delay hours.
Quality incident root cause analysis
When a quality incident occurs, AI links the batch, machine, operator, parameters, and material — and proposes the probable root cause within minutes without manual searching.
Capacity utilisation forecast
AI simulates future load based on accepted orders and predicted downtime — and alerts when capacity will be overloaded or underutilised.
Example answers
Which work orders are delayed or at risk today?
I identified 5 work orders at risk: 🔴 WO-441 — Steel frame 12× — DELAYED: Material STEEL PLATE short by 240 pcs. Delivery: 9 days. Alternative: ALU-PLATE (stock: 580 pcs, compatible specs). Suggestion: Switch material + prioritise WO-438 (less critical, frees capacity). 🔴 WO-437 — Hydraulics assembly — Machine H-02 OEE 51% (target: 78%). Root cause: tool change takes 2× longer than norm. Delay estimate: +6 hrs. 🟡 WO-452 — Surface treatment — Waiting for free slot at M-07 (bottleneck). Queue estimate: 14 hrs. Suggestion: Move to shift work. 🟡 WO-431 — Welding — Operator A. Kovač on sick leave. Suggestion: Reassign to WO-429. 🟠 WO-448 — Packaging — Quality incident from same batch as WO-446 (last week). AI detected same pattern: temperature +2°C above norm. Check furnace sensor P-3.
Why did machine M-07's OEE drop this week?
Analysis of machine M-07 OEE drop (week 27, 2026): OEE: 61.4% (previous week: 79.2%) — drop: −17.8 percentage points Breakdown by component: • Availability: 74% → 68% (−6 pp) — 3 unplanned stops totalling 4.2 hrs • Performance: 94% → 82% (−12 pp) ← MAIN ROOT CAUSE • Quality: 97% → 92% (−5 pp) Root causes from log analysis: 1. Performance (−12 pp): Batch changeover WO-447 required 38 min re-setup (norm: 15 min). 3 different operators in 5 days → inconsistent setup procedure. 2. Stops (−6 pp): 2× pressure sensor fault (ID: ERR-0441), 1× forklift wait. 3. Quality (−5 pp): Batch WO-446 — 4.2% scrap. Same pattern as incident 2026-03. Recommendations: • Standardise M-07 setup protocol (SOP update required). • Inspect pressure sensor ID ERR-0441 — likely probe wear. • Assign M-07 a dedicated forklift route (saves ~1.8 hrs/week waiting).
Production impact
Average Entexia customer results after 3 months.
Tomorrow morning you know which order is at risk.
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